A patient gets a genomic test. The result comes back. Now what?
Someone has to classify the variant. Match it to a therapy. Check clinical trials. Route the billing. Document everything for regulators. Update the plan when new data arrives.
That cascade, not the test itself, is where precision medicine lives or dies.
The science works. Sequencing costs fell 99%. AI reads pathology slides. Multi-cancer detection is entering clinical practice.
What doesn't work is what happens after the result is generated.
Consider a patient with an EGFR L858R mutation in non-small cell lung cancer. The variant is identified. But which therapies apply? Erlotinib, osimertinib, afatinib: each with different evidence levels, different trial eligibility criteria, different payer coverage rules. Is there a clinical trial recruiting for this exact mutation and stage? Does the patient's co-mutation profile (TP53? MET amplification?) change the recommendation? Answering these questions today requires a genetic counselor, a tumor board, a trial coordinator, and a billing specialist, all working in parallel across disconnected systems. The test took hours. The execution takes weeks.
The Structural Absence
Every organization rebuilds the same workflow logic from scratch. Every program re-implements variant classification, trial matching, billing routing, and compliance documentation independently.
This is the central problem. Not a tooling gap. A structural absence. There is no shared execution layer for precision medicine.
Veridata provides that execution layer: deterministic, auditable, and shared.